1. Field of the Invention
The present invention relates to a maximum likelihood decoding method and a maximum likelihood decoder for obtaining original information by decoding a reproduced signal reproduced from a recording medium or a signal obtained through a transmission medium. In particular, the present invention relates to a maximum likelihood decoding method and a maximum likelihood decoder applying partial response maximum likelihood decoding.
2. Description of the Related Art
In general, a reproducing apparatus used in a data recording/reproducing technique using various recoding media includes a pickup device for reading a signal recorded in a recording medium as a reproduced signal and a decoder for decoding the reproduced signal read by the pickup device so as to obtain original binary data.
Also, with an increase in the recording density in such a recording/reproducing technique, a partial response maximum likelihood (PRML) decoding method, which realizes high decoding reliability, has been adapted in decoders for decoding a reproduced signal so as to obtain original recorded data.
The PRML technique is realized by combining a partial response method, in which data sequences are associated with predetermined levels in units of bits, and a maximum likelihood decoding method, in which a data sequence is selected from among all possible data sequences so that a reference signal generated by a partial response becomes most approximate to an actual reproduced signal.
That is, in the partial response method, data sequences are compiled in units of sequential bits so as to associate a possible bit pattern to a reproduced-signal level.
For example, data is represented by dn and a sample generated by sampling a reproduced signal is represented by rn. n is the number of data. In this case, dn corresponds to the input of partial response and rn corresponds to the output of partial response. In the partial response, four sequential bits are associated with the level of the reproduced signal.
In order to obtain the output of partial response, the input four sequential bits are added in order by multiplying the four bits by weights a, b, b, and a, respectively. This can be represented by the following equation.rn=adn+bdn+1+bdn+2+adn+3  (1)This partial response is represented by PR (a b b a).
On the other hand, in the maximum likelihood decoding method, all possible data sequences are converted into reference signals of a reproduced signal through a predetermined partial response. Then, from among the reference signals, a reference signal that is the most approximate to a sample sequence of an actually detected reproduced signal is selected, and then the selected reference signal is decoded. Herein, the reference signals generated from the data sequences are ideal reproduced signals without noise.
The maximum likelihood decoding is an algorithm for selecting a reference signal that is the most likely to be an original reference signal from among all possible data sequences, in the condition that a detected reproduced signal is a reference signal to which noise is added (conditional probability). The conditional probability is calculated based on a metric, which can be obtained by the following equation.mn=(r˜n−rn)2  (2)
Herein, r˜n is a sample value of a reproduced signal detected at time n, and rn is a sample value of a reference signal at time n.
In actual maximum likelihood decoding, the sum of metrics is obtained instead of the conditional probability, and a data sequence for minimizing the sum is output. Also, instead of calculating metrics of all data sequences, a data sequence is selected or not selected at each channel clock so as to determine a final data sequence. Such a data sequence searching algorithm is realized by a Viterbi algorithm.
The above-described PRML is effective for random noise. However, noise in a recording/reproducing channel includes not only random noise but also noise having a frequency characteristic. Accordingly, measures should be taken to control such noise.
FIG. 12 is a block diagram showing a recording/reproducing system of a known art and noise generated therefrom. As shown in FIG. 12, media noise is caused at a recording medium 12A and system noise is caused at a pickup 12B and a maximum likelihood decoder 12C. The noise caused in the recording/reproducing system includes two types of noise: the system noise and the media noise. Therefore, the following equation can be obtained.Ntotal=Nsystem+Nmedia  (3)Herein, Ntotal represents total noise, Nsystem represents system noise, and Nmedia represents media noise.
The system noise is generated from noise caused by a detector, an electrical circuit, and deviation of the level of PRML, and is usually considered to be random noise. In the known PRML, decoding which is effective for such random noise can be realized.
On the other hand, the media noise is considered to be caused mainly by defects of a medium, crosstalk, and fluctuation of reflectivity. In general, media noise is different from system noise, and is added to a reproduced signal through a transmission medium having a frequency characteristic. Therefore, the media noise is random noise on media, but the media noise becomes noise having a frequency characteristic and a temporal correlation in a reproduced signal.
For example, when media noise passes through a system for realizing the above-described PR (a b b a), if the noise at an n-th channel bit on a medium is represented by nn, noise Nn in an n-th sample of a reproduced signal is represented by the following equation.Nn=ann+bnn+1+bnn+2+ann+3  (4)In this case, even if the media noise nn itself is random noise, that media noise in the reproduced signal has an emphasized low-frequency component.
FIGS. 13A and 13B show examples of the waveforms of a signal containing media noise and a signal containing system noise.
FIG. 13A shows a signal generated by adding the media noise represented by the equation (4) to the above-described equation (1). Also, FIG. 13B shows a signal generated by adding random noise to the equation (1). Herein, the SN ratio of media noise to a data signal is equal to the SN ratio of system noise to a reproduced signal. Also, an ideal signal without noise is indicated by a solid line.
By comparing the waveforms in FIGS. 13A and 13B, it can be found that the signal with media noise maintains the original state better than the signal with system noise. This is because the media noise becomes noise having a low-frequency and is virtually offset, and as a result, a relative level can be maintained.
In general, noise in a recording/reproducing system is temporarily correlated noise due to the frequency characteristic of the channel, and thus the signal waveform can be maintained relatively well compared to the case where random noise of the same S/N ratio exists.
However, the known PRML is effective to random noise, but is not so effective to offset noise. Also, even if noise has a frequency characteristic, the characteristic cannot be used positively.
That is, the known PRML is not the optimal decoding method in case noise has a frequency characteristic. Accordingly, a more appropriate decoding method is required to be developed for controlling noise with a frequency characteristic.
The noise which is obtained after processing of partial response PR (a b b a) having a characteristic of attenuating a high-frequency band has an emphasized low-frequency component, and thus the noise is offset. As a result, the signal level is relatively maintained, as described above.
Accordingly, a partial response having a frequency characteristic for attenuating a low-frequency band such as a differential waveform is proposed as a partial response in which relative levels can be compared.
If the partial response PR (a b b a) can be realized compared to the frequency characteristic in a step of transferring data, a partial response PR (a b−a 0 a−b −a), which is a differential response between the partial response PR (a b b a) and a response shifted by 1 clock, can also be realized.
Further, a reproduced signal generated by the PR (a b−a 0 a−b −a) can be represented by the following equation.rn=a(dn−dn+4)+(b−a)(dn+1−dn+3)   (5)
The response of the equation (5) is generated by subtraction by shifting the response of PR (a b b a) by 1 clock. Therefore, if the reproduced signal can be equalized to PR (a b b a), the reproduced signal can also be equalized to PR (a b−a 0 a−b −a).
Accordingly, by comparing the relative level of amplitude by using a partial response obtained as a temporal difference of the above-described partial response, more effective maximum likelihood decoding can be realized.
However, if the noise in an n-th sample of a reproduced signal is represented by Nn, the media noise nn obtained through partial response PR (a b−a 0 a−b −a) processing contributes like this:Nn=a(nn−nn+4)+(b−a)(nn+1−nn+3)   (6)
Therefore, a higher frequency component is emphasized in the noise obtained through the partial response PR (a b−a 0 a−b −a), compared to the partial response PR (a b b a).
Also, by obtaining a difference, a high-frequency component of random noise is amplified. Therefore, maximum likelihood decoding using a partial response generated by using a temporal difference is not always more effective than maximum likelihood decoding using an original partial response. Accordingly, maximum likelihood decoding using an original partial response and maximum likelihood decoding using a partial response using a temporal difference are combined.
The followings are summary of the above-description.    (1) Noise caused in a recording/reproducing system includes random system noise and non-random media noise. Therefore, PRML which is effective for the non-random noise is required.    (2) In the original partial response, a low-frequency component of media noise is emphasized, and thus the noise in a reproduced signal has many low-frequency components.    (3) By obtaining a time difference of the original partial response, noise of a low-frequency component is attenuated, while noise of a high-frequency component is emphasized.    (4) Accordingly, by developing maximum likelihood decoding using both of the original partial response and the time-differential partial response, effects can be expected.